Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method for associating keywords to items of content based on search requests, comprising: receiving, by one or more servers from a first client device, a first request for a first item of content; maintaining, by the one or more servers and in a data storage, an association between the first item of content and keywords extracted from a plurality of Internet search requests transmitted by a plurality of second client devices within a predetermined temporal window of playback of the first item of content by the plurality of second client devices, wherein the keywords are extracted by identifying the plurality of second client devices on which the first item of content was presented; identifying the plurality of Internet search requests performed by the plurality of second client devices within the predetermined temporal window of playback of the first item of content by the plurality of second client devices; selecting the keywords from the plurality of Internet search requests based on a frequency of the keywords included in the plurality of Internet search requests; retrieving, by the one or more servers from the data storage and responsive to receiving the first request for the first item of content, at least one keyword associated with the first item of content; retrieving, by the one or more servers from the data storage, a second item of content selected based on the retrieved at least one keyword; and providing, by the one or more servers to the first client device, the second item of content for presentation in response to the first request for the first item of content.
This invention relates to a system for associating keywords with digital content based on user search behavior to improve content recommendations. The method involves tracking search requests made by users while they are consuming specific content, such as videos or articles, within a defined time window. The system identifies devices where the content was presented, then analyzes search queries performed by those devices during playback. Keywords are extracted from these searches based on their frequency, and the content is tagged with the most relevant keywords. When a user requests the same content, the system retrieves the associated keywords and uses them to fetch and recommend related content. This approach leverages real-time search activity to dynamically associate metadata with content, enhancing recommendation accuracy by reflecting user interests during consumption. The system operates on a server infrastructure, storing keyword associations in a database and processing requests to deliver personalized content suggestions.
2. The method of claim 1 , further comprising: calculating, by the one or more servers, a difference between a time that the first item of content was played by a second client device of the plurality of second client devices and a time that an Internet search request of the plurality of Internet search requests was performed by the second client device of the plurality of second client devices; and comparing, by the one or more servers, the difference to a predetermined threshold value to determine whether the Internet search request transmitted by the second client device of the plurality of second client devices occurred within the predetermined temporal window of playback.
This invention relates to analyzing user behavior in relation to content playback and internet search activity. The system monitors content playback on multiple client devices and tracks associated internet search requests to determine if searches are performed within a specific time window after content playback. The method involves calculating the time difference between when a piece of content was played on a client device and when a subsequent internet search was performed by the same device. This difference is then compared to a predefined threshold value to assess whether the search occurred within the temporal window of playback. The system can identify correlations between content consumption and subsequent search behavior, enabling insights into user intent or content effectiveness. The invention may be used for targeted advertising, content recommendation, or behavioral analysis by determining how quickly users search for related information after engaging with specific content. The method enhances understanding of user engagement patterns and the impact of content on subsequent online activity.
3. The method of claim 1 , further comprising: aggregating, by the one or more servers, the plurality of Internet search requests transmitted by the plurality of second client devices within a plurality of predetermined temporal windows of playback of a plurality of respective items of content by the plurality of second client devices.
This invention relates to analyzing internet search behavior correlated with media content consumption. The problem addressed is the lack of insight into how users search for information while engaging with digital content, which limits targeted advertising and content recommendations. The method involves tracking search requests from multiple client devices during playback of various content items. These search requests are aggregated within predefined time intervals (temporal windows) of the content playback. The system identifies patterns in search queries that occur during specific segments of media, enabling correlations between content and user information needs. This data can be used to improve content recommendations, targeted advertising, or user engagement strategies. The method may also include analyzing the aggregated search data to detect trends or anomalies in user behavior during different content segments. For example, if many users search for product information during a specific scene, this indicates high commercial relevance. The system can then prioritize related advertisements or suggest similar content to users. Additionally, the method may involve filtering or categorizing search queries to focus on relevant topics, improving the accuracy of behavioral insights. The aggregated data can be stored and processed to generate reports or actionable insights for content providers and advertisers.
4. The method of claim 1 , further comprising: determining, by the one or more servers, that at least one of the plurality of Internet search requests comprises a search for a product.
This invention relates to internet search systems that analyze user search requests to identify product-related queries. The method involves processing multiple internet search requests to detect when at least one of them is seeking a product. The system uses one or more servers to evaluate the search requests, likely employing natural language processing or keyword matching to determine if a query is product-related. This could involve identifying terms like "buy," "purchase," or specific product names. The method may also include additional steps such as categorizing the product, retrieving relevant product information, or displaying targeted advertisements. The goal is to enhance search results by recognizing commercial intent and providing more relevant responses, such as product listings, pricing, or availability details. This improves user experience by reducing the need for additional searches and streamlining the path to purchase. The system may also track user behavior to refine future product-related search detections. The invention is particularly useful for e-commerce platforms and search engines aiming to optimize product discovery and conversion rates.
5. The method of claim 1 , wherein the retrieving the second item of content comprises retrieving an advertisement for a product.
This invention relates to content retrieval systems, specifically methods for dynamically selecting and presenting secondary content, such as advertisements, based on user interactions with primary content. The problem addressed is the need for more relevant and contextually appropriate secondary content delivery, improving user engagement and conversion rates. The method involves analyzing user interactions with a first item of content, such as a webpage or media file, to determine contextual or behavioral cues. Based on this analysis, a second item of content is retrieved and presented to the user. In this specific embodiment, the second item of content is an advertisement for a product. The retrieval process may involve matching the user's interaction patterns, preferences, or contextual data with advertisement criteria to select the most relevant ad. The system may also track subsequent user interactions with the advertisement to refine future content recommendations. The method may include additional steps such as filtering advertisements based on user preferences, historical data, or real-time behavior. The system could also prioritize advertisements based on relevance scores or campaign objectives. The goal is to enhance the user experience by delivering targeted, non-intrusive advertisements that align with the user's current context or interests. This approach improves ad performance metrics, such as click-through rates and conversion rates, while maintaining a seamless user experience.
6. The method of claim 1 , wherein at least one of the plurality of Internet search requests transmitted by a second client device of the plurality of second client devices within the predetermined temporal window of playback of the first item of content comprises an Internet search request transmitted by the second client device of the plurality of second client devices while the first item of content was played.
This invention relates to analyzing Internet search requests made by multiple client devices during the playback of digital content. The problem addressed is the need to correlate user search behavior with content consumption in real-time or near-real-time to improve content recommendations, advertising, or user engagement. The method involves monitoring a plurality of second client devices that are playing a first item of content, such as a video, audio stream, or other media. During a predetermined temporal window of playback—either before, during, or after the content is played—search requests from these devices are captured. The method specifically tracks search requests made while the content is actively being played, indicating a potential correlation between the content and the user's search activity. The captured search requests are then analyzed to identify patterns, trends, or specific queries that may be influenced by the content being consumed. This data can be used to refine content recommendations, target advertisements, or enhance user experience by understanding how content consumption drives subsequent search behavior. The method may also involve comparing search requests across multiple devices to identify broader trends or differences in user engagement with the content.
7. A system for associating keywords to items of content based on search requests, comprising: one or more servers in communication with a data storage and with a plurality of second client devices, the one or more servers executing operations to receive, from a first client device, a first request for a first item of content; maintain, in a data storage, an association between the first item of content and keywords extracted from a plurality of Internet search requests transmitted by a plurality of second client devices within a predetermined temporal window of playback of the first item of content by the plurality of second client devices, wherein the keywords are extracted by identifying the plurality of second client devices on which the first item of content was presented; identifying the plurality of Internet search requests performed by the plurality of second client devices within the predetermined temporal window of playback of the first item of content by the plurality of second client devices; selecting the keywords from the plurality of Internet search requests based on a frequency of the keywords included in the plurality of Internet search requests; retrieve, from the data storage and responsive to receiving the first request for the first item of content, at least one keyword associated with the first item of content; retrieve, from the data storage, a second item of content selected based on the retrieved at least one keyword; and provide, to the first client device, the second item of content for presentation in response to the first request for the first item of content.
This system relates to content recommendation based on search behavior. The problem addressed is improving content discovery by leveraging search queries made by users who recently engaged with specific content. The system operates by tracking search requests from multiple client devices during a predefined time window around when those devices played a particular item of content. Keywords are extracted from these search requests, filtered by frequency, and stored in association with the content. When a user requests that content, the system retrieves the associated keywords and uses them to identify and recommend related content. The recommendation process involves matching the keywords to other content items in the database, ensuring relevance based on collective search behavior. This approach enhances content discovery by dynamically linking items to real-time search trends from engaged users, improving personalization and relevance in recommendations. The system requires servers to manage data storage, process search requests, and handle content retrieval and delivery to client devices.
8. The system of claim 7 , wherein the one or more servers execute operations to: calculate a difference between a time that the first item of content was played by a second client device of the plurality of second client devices and a time that an Internet search request of the plurality of Internet search requests was performed by the second client device of the plurality of second client devices; and compare the difference to a predetermined threshold value to determine whether the Internet search request transmitted by the second client device of the plurality of second client devices occurred within the predetermined temporal window of playback.
This invention relates to a system for analyzing user behavior by correlating content playback with subsequent online search activity. The system monitors content consumption on client devices and tracks internet search requests performed by the same devices. Specifically, the system calculates the time difference between when a content item was played on a client device and when a subsequent internet search was performed by that same device. This time difference is then compared to a predefined threshold value to determine whether the search occurred within a specific temporal window following content playback. The system uses this analysis to identify potential relationships between content exposure and user search behavior, which can be leveraged for targeted advertising, content recommendations, or behavioral insights. The invention builds upon a broader system that collects and processes content playback data and search request data from multiple client devices, enabling large-scale behavioral analysis. The temporal correlation helps distinguish between searches that may be directly influenced by content consumption versus unrelated searches, improving the accuracy of behavioral predictions.
9. The system of claim 7 , wherein the one or more servers execute operations to: determine that at least one of the plurality of Internet search requests comprises a search for a product.
This invention relates to a system for processing Internet search requests to identify and respond to product-related queries. The system includes one or more servers configured to analyze incoming search requests to determine whether they pertain to a product. When a product-related search is detected, the system generates a response that includes product information, such as details about the product, pricing, availability, or related offers. The system may also track user interactions with the search results to refine future responses. The servers may further categorize the search requests based on user intent, such as whether the user is seeking to purchase, compare, or research a product. The system may integrate with external databases or e-commerce platforms to retrieve real-time product data. Additionally, the servers may apply machine learning techniques to improve the accuracy of product detection and response generation over time. The system aims to enhance the efficiency of online product searches by providing relevant and actionable information directly within search results, reducing the need for users to navigate multiple websites.
10. The system of claim 7 , wherein the retrieving the second item of content comprises retrieving an advertisement for a product.
A system retrieves and displays content based on user interactions with a first item of content, such as a video or webpage. The system analyzes user engagement metrics, such as viewing duration or interaction frequency, to determine relevance. When a user interacts with the first item, the system retrieves a second item of content related to the first item. In some cases, the second item is an advertisement for a product. The system may also adjust the display of the second item based on additional factors, such as user preferences or historical behavior. The goal is to improve content relevance and user engagement by dynamically selecting and presenting related content, including advertisements, in response to user actions. The system may operate in digital media platforms, social networks, or e-commerce environments where personalized content delivery enhances user experience and conversion rates. The retrieval process ensures that the second item is contextually relevant to the first item, increasing the likelihood of user interest and interaction.
11. The system of claim 7 , wherein the one or more servers execute operations to: aggregate the plurality of Internet search requests transmitted by the plurality of second client devices within a plurality of predetermined temporal windows of playback of the first item of content by the plurality of second client devices.
This invention relates to a system for analyzing Internet search requests generated during content playback. The system addresses the problem of understanding user behavior and interests by tracking search queries made while users engage with digital content. The system includes one or more servers that process search requests from multiple client devices during playback of a content item, such as a video or audio stream. The servers aggregate these search requests within predefined time intervals, or temporal windows, of the content's playback. This allows for the analysis of user search patterns at specific moments in the content, revealing correlations between content segments and user interests. The system may also identify and filter out irrelevant or duplicate search requests to improve data accuracy. By analyzing aggregated search data, the system enables content providers to gain insights into user engagement, optimize content delivery, and tailor recommendations based on real-time user behavior. The invention enhances content personalization and improves user experience by leveraging search query data as a proxy for user intent during content consumption.
12. The system of claim 7 , wherein the plurality of Internet search requests transmitted by a second client device of the plurality of second client devices within the predetermined temporal window of playback of the first item of content comprises an Internet search request transmitted by the second client device of the plurality of second client devices while the first item of content was played.
This system relates to analyzing Internet search behavior correlated with content playback. The problem addressed is the lack of insight into how users engage with online content and related search activities, which can inform content recommendations, advertising, and user experience optimization. The system monitors a plurality of second client devices to detect Internet search requests made during or shortly after the playback of a first item of content on a first client device. The search requests are transmitted within a predetermined temporal window of the content's playback, meaning they occur either during playback or shortly after. The system tracks these searches to identify patterns or trends in user behavior, such as searches for related topics, products, or additional content. This data can be used to refine content recommendations, target advertisements, or improve the relevance of suggested content. The system may also analyze the frequency, timing, and nature of these search requests to determine their correlation with specific segments of the content. For example, if a particular scene or topic in the content triggers a spike in searches, the system can flag that segment for further analysis or promotional purposes. The data collected can be aggregated across multiple second client devices to identify broader trends or user preferences. By linking search behavior to content consumption, the system provides valuable insights into how users interact with digital content and what additional information or resources they seek, enabling more personalized and effective content delivery.
Unknown
October 1, 2019
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